A Geometrically Constrained Point Matching based on View-invariant Cross-ratios, and Homography

TL;DR

This paper introduces a geometrically constrained point matching method using view-invariant cross-ratios and homography to improve the verification of initial feature matches in images, enhancing robustness in planar region detection.

Contribution

It proposes a novel verification algorithm based on view-invariant cross-ratios and shape matching of pentagons for more accurate point correspondence verification.

Findings

Effective in various scenes with single and multiple planar regions

Improves robustness of point matching verification

Achieves accurate planar region estimation

Abstract

In computer vision, finding point correspondence among images plays an important role in many applications, such as image stitching, image retrieval, visual localization, etc. Most of the research worksfocus on the matching of local feature before a sampling method is employed, such as RANSAC, to verify initial matching results via repeated fitting of certain global transformation among the images. However, incorrect matches may still exist, while careful examination of such problems is often skipped. Accordingly, a geometrically constrained algorithm is proposed in this work to verify the correctness of initially matched SIFT keypoints based on view-invariant cross-ratios (CRs). By randomly forming pentagons from these keypoints and matching their shape and location among images with CRs, robust planar region estimation can be achieved efficiently for the above verification, while correct and incorrect matches of keypoints can be examined easily with respect to those shape and location matched pentagons. Experimental results show that satisfactory results can be obtained for various scenes with single as well as multiple planar regions.